The need to improve our attention to scale of resolution in grouse research
Bibliographic record
Abstract
A principal focus of ecological research should be to learn the influence of scale on ecological processes and to determine appropriate spatial and temporal scales with which to study the problem of interest. To date, little attention has been given to scale issues in grouse research, and we conclude that this is a major weakness. The problem affects not only our interpretations of individual events and processes, but also our interpretation of relative effects of various factors and processes, which likely act at different scales. Investigators should provide evidence for the choices made for the scale of resolution/grain used in field studies and analyses, and should conduct analyses at multiple scales. The current research problem also hinders the quality of forecasts that can be made about management interventions. To help illustrate the effect that scale of resolution can have upon research results, we provide two examples on grouse, one temporal and the other spatial.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.347 | 0.604 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.015 | 0.043 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".